Characterzing Audio Adversarial Examples
using Temporal Dependency

In ICLR 2019

Abstract

Recent studies have highlighted adversarial examples as a ubiquitous threat to different neural network models and many downstream applications. Nonetheless,
as unique data properties have inspired distinct and powerful learning principles,
this paper aims to explore their potentials towards mitigating adversarial inputs.
In particular, our results reveal the importance of using the temporal dependency
in audio data to gain discriminate power against adversarial examples. Tested on
the automatic speech recognition (ASR) tasks and three recent audio adversarial
attacks, we find that (i) input transformation developed from image adversarial defense provides limited robustness improvement and is subtle to advanced attacks;
(ii) temporal dependency can be exploited to gain discriminative power against
audio adversarial examples and is resistant to adaptive attacks considered in our
experiments. Our results not only show promising means of improving the robustness of ASR systems but also offer novel insights in exploiting domain-specific
data properties to mitigate negative effects of adversarial examples.